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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef __ONERT_BACKEND_CPU_TENSOR_H__
#define __ONERT_BACKEND_CPU_TENSOR_H__
#include <backend/cpu_common/Tensor.h>
#include <ir/Data.h>
namespace onert
{
namespace backend
{
namespace cpu
{
using Tensor = cpu_common::Tensor;
/**
* @brief Class that uses data from external memory that is not managed by a backend
* instead of allocating and copying the data. ExternalTensor's data pointer points to
* an address of memory such as where memory is already allocated, or mmapped area.
* This is meaning that ExternalTensor can take all of types' ir::Data.
* To support this, assume below things no padding, always NHWC layout,
* constant tensor and not dynamic.
*/
class ExternalTensor : public Tensor
{
public:
ExternalTensor() = delete;
virtual ~ExternalTensor();
public:
ExternalTensor(const ir::OperandInfo &info, const ir::Layout layout)
: Tensor(info, layout, nullptr)
{
assert(_layout == ir::Layout::NHWC);
assert(_info.isConstant());
assert(_info.isDynamic() == false);
}
public:
/**
* @brief set Data to be shared from external so that this ExternalTensor will not be
* allocated on CPU backend
* @param[in] data data of Operand to be set
*/
void setData(const std::shared_ptr<ir::Data> data)
{
assert(data != nullptr);
_data = data;
// Note. Some op such as cker::Conv could take buffer as nullptr.
// That's why _buffer also would be used
_buffer = const_cast<uint8_t *>(_data->base());
}
public:
uint8_t *buffer() const override { return _buffer; }
bool is_constant() const override { return true; }
bool is_dynamic() const override { return false; }
void set_dynamic() override
{
throw std::runtime_error("This tensor does not support changing dynamic");
}
void setShape(const ir::Shape &) override
{
throw std::runtime_error("This tensor does not support changing shape");
}
void increase_ref() override { ++_num_references; }
void decrease_ref() override
{
assert(_data != nullptr);
assert(_num_references > 0);
--_num_references;
if (_num_references == 0)
{
_data.reset();
_buffer = nullptr;
}
}
/**
* @brief Reset reference count to zero and release data
*/
void reset_ref() override
{
assert(_data != nullptr);
assert(_num_references > 0);
_num_references = 0;
_data.reset();
_buffer = nullptr;
}
int32_t num_references() override { return _num_references; }
private:
std::shared_ptr<const ir::Data> _data;
};
} // namespace cpu
} // namespace backend
} // namespace onert
#endif // __ONERT_BACKEND_CPU_TENSOR_H__
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